Underwater target 6D State Estimation via UUV Attitude Enhance Observability

2025-06-16

AI Summary

This paper addresses the challenge of persistent six-degree-of-freedom (6D) relative state estimation for a non-cooperative underwater target by a single unmanned underwater vehicle (UUV) operating in GPS-denied, a priori unknown underwater environments. We propose a purely passive localization and attitude tracking method relying solely on time-series range measurements from two monostatic sonars. Our approach innovatively integrates observability-enhancing attitude regulation with Lyapunov-stable closed-loop tracking control—achieving, for the first time under pure range-only measurements, both improved 6D state observability and long-term error suppression. The state estimation framework employs an extended Kalman filter, rigorously informed by nonlinear observability analysis and co-designed autonomous control laws. Simulation results demonstrate over 40% reduction in 6D estimation error and more than triple the divergence time compared to conventional methods, significantly enhancing robustness and long-term stability.

Technology Category

Intelligent Robots: State EstimationReasoning under Uncertainty: Uncertainty RepresentationsPlanning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systemsGraph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Accurate relative state observation of Unmanned Underwater Vehicles (UUVs) for tracking uncooperative targets remains a significant challenge due to the absence of GPS, complex underwater dynamics, and sensor limitations. Existing localization approaches rely on either global positioning infrastructure or multi-UUV collaboration, both of which are impractical for a single UUV operating in large or unknown environments. To address this, we propose a novel persistent relative 6D state estimation framework that enables a single UUV to estimate its relative motion to a non-cooperative target using only successive noisy range measurements from two monostatic sonar sensors. Our key contribution is an observability-enhanced attitude control strategy, which optimally adjusts the UUV's orientation to improve the observability of relative state estimation using a Kalman filter, effectively mitigating the impact of sensor noise and drift accumulation. Additionally, we introduce a rigorously proven Lyapunov-based tracking control strategy that guarantees long-term stability by ensuring that the UUV maintains an optimal measurement range, preventing localization errors from diverging over time. Through theoretical analysis and simulations, we demonstrate that our method significantly improves 6D relative state estimation accuracy and robustness compared to conventional approaches. This work provides a scalable, infrastructure-free solution for UUVs tracking uncooperative targets underwater.

Problem

• Estimating 6D state of underwater targets using UUV attitude
• Overcoming sensor noise and drift in underwater localization
• Enhancing observability for single UUV tracking uncooperative targets

Innovation

• UUV attitude control enhances observability
• Lyapunov-based tracking ensures long-term stability
• 6D state estimation via monostatic sonar sensors